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This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

Despite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.

A systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.

The study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.

The findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.

This study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.

This paper aims to identify the different system approach using Building Information Modelling (BIM) technology that is equipped with decision making processes. Maintenance planning and management are integral components of the construction sector, serving the broader purpose of post-construction activities and processes. However, as Precast Concrete (PC) construction projects increase in scale and complexity, the interconnections among these activities and processes become apparent, leading to planning and performance management challenges. These challenges specifically affect the monitoring of façade components for corrective and preventive maintenance actions.

The concept of maintenance planning for façades, along with the main features of information and communication technology tools and techniques using building information modeling technology, is grounded in the analysis of numerous literature reviews in PC building scenarios.

This research focuses on an integrated system designed to analyze information and support decision-making in maintenance planning for PC buildings. It is based on robust data collection regarding concrete façades' failures and causes. The system aims to provide appropriate planning decisions and minimize the risk of façade failures throughout the building's lifetime.

The study concludes that implementing a research framework to develop such a system can significantly enhance the effectiveness of maintenance planning for façade design, construction and maintenance operations.

The purpose of this study is to explore how digital construction policy (DCP) drives enterprise green innovation (EGI) from an information processing theory (IPT) perspective, including the mediating mechanisms of market information accessibility and operational risk, the moderating role of intellectual property protection (IPP) and product market competition (PMC) and the heterogeneous effects of ownership, Internet development and managerial ability.

Based on the matched panel data of A-share listed enterprises from 2011 to 2019 and the Broadband China policy as a quasinatural experiment, this study investigates the impact of DCP on EGI by constructing a multi-time point difference-indifferences (DID) model.

Digital construction policies can significantly promote EGI. DCP works in two fundamental ways, namely by increasing market information accessibility and reducing operational risk. IPP and PMC significantly increased the contribution of digital construction policies to EGI. Heterogeneity analysis found that digital technology has a stronger promotion effect for SOEs, high-managerial-ability enterprises and enterprises in regions with low Internet development levels.

The study provides new insights about the antecedents of EGI from a DCP perspective. It also enlightens emerging economies to actualize green innovation under the digital wave.

From the perspective of IPT, this study explains the mechanism of DCP-driven EGI. It enhances understanding of the relationship between DCP and EGI.

The aim of this paper is to review and analyze the research literature on the health and safety issues of migrant workers in the construction industry from 2000 to 2022.

5 steps method is used to conduct a systematic review to achieve the objectives. After scanning two authoritative search engines “Web of Science” and “Scope”, 60 articles are selected from 225 publications for identification and review. These identified articles are classified by research fields, countries and time span.

The review finds that with the increasing influence of migrant construction workers, the number of publications on the health and safety of migrant workers has shown a rapid upward trend. Moreover, language barriers are the most dominant safety risk factors encountered by on-site migrant workers. This systematic literature review also summarizes the definition of migrant workers and solutions to reduce safety risk factors.

The research data on the health and safety issues and risk factors of migrant workers in the construction industry is still limited. This literature review summarizes the research trends and contributions of the literature in this field in the past 22 years and provides theoretical support for future research on the safety management of the migration construction field.

This study aims to investigate the effects of peers on corporate payout policies in one of the largest emerging markets – India. It also examines the motives for mimicking payout decisions.

The sample is composed of 3,024 non-financial and non-government firms listed on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) for the period 1995 to 2020. To encounter the endogeneity problem, the instrumental variable technique based on peer firms' idiosyncratic risk is used to estimate the effects of peers on firms' payout policy. To define peer reference groups, the authors use the basic industry classification of the firms.

The results indicate a significant positive impact of peers on firms' dividend policies in India. A firm with all dividend-paying peers is more likely to declare dividends than the one with no dividend-paying peers. Further, peer effects are found to be more pronounced amongst larger and older firms, thus supporting the rivalry theory of mimicking.

To the best of the authors' knowledge, the present study is the first of its kind that attempts to understand peer effects on payout decisions in an emerging market India, that offers a unique institutional setting. Moreover, the authors extend the existing literature by investigating the peer effects on a firm's payout policies considering various firm-level characteristics, such as growth opportunity, cash holding, financial constraint and profitability, which previous studies have not taken into consideration. These results provide additional insights into the heterogeneity and motives behind peer effects.

The ancient town of Lijiang is a representative place of ethnic minorities in China’s southwest border area jointly built by many ethnic groups. Its rich and diversified history, culture and architecture as well as its artistic and spiritual values need to be better retained and explored.

The protection and inheritance of Lijiang’s cultural heritage will be improved through the construction of digital memory resources. To guide Lijiang’s digital memory construction, this study explores strategies of digital memory construction by analyzing four case studies of well-known memory projects from China and America.

From the case studies analysis, factors of digital memory construction were identified and compared. Factors led to the discussion of strategies for constructing the digital memory of Lijiang within its design, construction and service phases.

The ancient town of Lijiang is a famous historical and cultural city in China, and it is also a representative place of ethnic minorities in the border area jointly built by many ethnic groups. The rich culture should be preserved and digitalized to offer better use for the whole nation.

The lean startup approach (LSA) is extensively utilized by early-stage entrepreneurs, with “pivot” serving as a key pillar. However, there is a research gap concerning the boundary conditions impacting LSA and pivot decisions, especially when addressing societal challenges, as in the context of transformational entrepreneurship. In this regard, the healthcare sector, further compounded by a lack of research on startups and scale-ups, presents an embraced opportunity to provide multiple contributions for both theory and practice.

The present investigation employs a grounded approach to explore the experiences of the co-founders of a fast-growing Italian e-health startup. A narrative strategy was employed to organize conditions and evolving strategic action/interactions into three different pivoting phases of the startup – before the pivot, its enactment and aftermath – with primary and secondary data collected over a period of one year.

Pivoting in digital healthcare unfolded as a liminal experience marked by factors such as high regulation, multiple stakeholders, technological and symbolic ambivalence, resource-intensive demands and institutional actors acting as pathway pioneers, leading to an information overload and unforeseeable uncertainty to manage. These factors challenge entrepreneurs' ability to attain optimal distinctiveness, presenting the paradoxical need for vertical flexibility for scaling up.

By uniquely illuminating the sector’s constraints on entrepreneurial phenomena, this study provides a valuable guide for entrepreneurs and institutional actors in addressing societal challenges.

This study introduces a process model of transformational information crafting when pivoting, highlighting the role of entrepreneurs' transformational stance and platform-mediated solutions as engines behind strategies involving information breaking and transition, preceding knowledge-driven integration strategies.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.

The supply chain is undergoing a significant digital transformation to adapt to the increasingly digitalized and globalized business environment. To remain competitive in this evolving market, businesses must seamlessly integrate digital technologies throughout the supply chain, spanning all stages from procurement to distribution. This chapter delves into models and methodologies critical to digital supply chain (DSC) transformation, with a focus on advanced techniques such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and data analytics to boost the resilience and agility of supply chain operations. By leveraging practical examples and case studies, the chapter highlights the myriad enhancements digital transformation can introduce across diverse supply chain stages, including sourcing and after-sales service. Additionally, the chapter examines the complexities of cybersecurity, data integrity, and change management within the digital transformation framework, proposing strategies to address these challenges. The insights offered in this chapter will serve as a thorough guide for both practitioners and scholars in the supply chain field, equipping them to adeptly navigate the multifaceted arena of digital transformation.